Current use cases for machine learning in retail and consumer goods

This blog was co-authored by Marty Donovan.

Retail and consumer goods companies are seeing the applicability of machine learning (ML) to drive improvements in customer service and operational efficiency. For example, the Azure cloud is helping retail and consumer brands improve the shopping experience by ensuring shelves are stocked and product is always available when, where and how the consumer wants to shop. Learn more by reading Retail and consumer goods use case: Inventory optimization through SKU assortment + machine learning.

Here are common use cases for ML in retail and consumer goods, along with resources for getting started with ML in Azure.

8 ML use cases to improve service and provide benefits of optimization, automation and scale

1. Inventory optimization through SKU assortment + machine learning ensure shelves are stocked and best products are always available for purchase.
2. Recommendation Engine – Train Matchbox Recommender to modernize engine capabilities for relevant product and service offerings which can generate incremental revenue.
3. Visual Search capitalizes on mobile-first, content rich, customer-centric search capabilities.
4. Sentiment Analysis can help companies improve their products and services by better understanding how their offering impact customers.
5. Fraud Detection to detect anomalies and other errors that signal dishonest behavior.
6. Demand forecasting by pricing optimization to meet consumer demand related by creating a demand forecast at various price points and business constraints to maximize potential profit.
7. Personalized offers improve the customer experience by offering relevant information which in turn provides retailers with improved data about the customer’s brand engagement.
8. Customer Churn Prediction to improve strategic decision making regarding customer engagement and lifetime value.

All of these use cases can be addressed using machine learning.

Machine learning on Azure

Customers can build artificial intelligence (AI) applications that intelligently process and act on data, often in near real time. This helps organizations achieve more through increased speed and efficiency. Here are some resources to help you get started.

• Azure Machine Learning services enable building, deploying, and managing machine learning and AI models using any Python tools and libraries.
• Azure Data Science Virtual Machines are customized VM images on Azure, loaded with data science tools used to build intelligent applications for advanced analytics.
• Azure Machine Learning Studio which comes with many algorithms out of the box.
• Azure AI Gallery, which showcases AI and ML algorithms and use cases for them.

Recommended next steps

• Complete the Azure Machine Learning services quickstart.
• Read Retail and consumer goods use case: Inventory optimization through SKU assortment + machine learning. This explains how consumer brands can leverage Azure to create to prevent stock-outs, ensuring shelves are stocked and products are always available.

Additional resources

• Download Machine learning algorithm cheat sheet to help choose an algorithm.
• What machine learning algorithm should I use? Read How to choose algorithms for Microsoft Azure Machine Learning for help answering this question.
• Machine learning at scale AI/ML expert Paige Bailey takes you on a tour of the services available on Azure. See how to take your predictive model to production, dynamically train it online with streaming updates, and add real-time data to your models from IoT sources. Paige covers Azure DataBricks, Batch AI, KubeFlow + AKS, Stream Analytics, and Event Hubs.
• Read Welcome to Machine Learning Server for an introduction to the Microsoft Machine Learning Server (formerly named “R Server”).
• Review the Solution templates for Machine Learning Server for industry-specific templates, including one for retail.

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Source: Azure Blog Feed